A voxel-wise meta-analysis of task-based functional MRI studies on impaired gain and loss processing in adults with addiction
Bibliographic record
Abstract
BACKGROUND: Disturbances in gain and loss processing have been extensively reported in adults with addiction, a brain disorder characterized by obsession with addictive substances or behaviours. Previous studies have provided conflicting results with respect to neural abnormalities in gain processing in addiction, and few investigations into loss processing. METHODS: We conducted voxel-wise meta-analyses of abnormal task-evoked regional activities in adults with substance dependence and gambling addiction during the processing of gains and losses not related to their addiction (mainly monetary). We identified 24 studies, including 465 participants with substance dependence, 81 with gambling addiction and 490 healthy controls. RESULTS: Compared with healthy controls, all participants with addictions showed hypoactivations in the prefrontal cortex, striatum and insula and hyperactivations in the default mode network during gain anticipation; hyperactivations in the prefrontal cortex and both hyper- and hypoactivations in the striatum during loss anticipation; and hyperactivations in the occipital lobe during gain outcome. In the substance dependence subgroup, activity in the occipital lobe was increased during gain anticipation but decreased during loss anticipation. LIMITATIONS: We were unable to conduct meta-analyses in the gambling addiction subgroup because of a limited data set. We did not investigate the effects of clinical variables because of limited information. CONCLUSION: The current study identified altered brain activity associated with higher- and lower-level function during gain and loss processing for non-addiction (mainly monetary) stimuli in adults with substance dependence and gambling addiction. Adults with addiction were more sensitive to anticipatory gains than losses at higher- and lower-level brain areas. These results may help us to better understand the pathology of gain and loss processing in addiction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".